Federated cross-domain recommendation aims to alleviate the problem of data sparsity and enable collaborative modeling of user behavior data from different platforms or institutions while ensuring data privacy. Most existing federated cross-domain recommendation methods rely on item IDs for modeling, ignoring the mining and utilization of item semantic information. In addition, due to the heterogeneity of data between different domains, the model is prone to domain bias and feature coupling problems during the aggregation process, which negatively impacts the recommendation performance. This paper proposes a dual-target federated cross-domain recommendation framework with semantic enhancement and disentangled contrastive learning. First, to utilize semantic information of items, item IDs features and text semantic features are jointly fused to enhance the item embedding representations. Second, we propose a user representation decoupling mechanism to explicitly decouple users preferences into shared and domain-specific preferences, thereby alleviating domain bias and feature coupling problems. Furthermore, we design a cross-domain contrastive learning module on the server side to enhance the consistency and transferability of shared representations between user representations across different domains. Experimental results show that the proposed algorithm performs significantly better than existing optimal methods on multiple real-world datasets, demonstrating its excellent performance in federated cross-domain recommendations.

With the rapid advancements in computer vision, high-resolution remote sensing imagery has become a crucial data source for object detection. Nevertheless, effectively utilizing limited computational resources and reducing the burden on satellite edge devices remains a significant challenge. To effectively reduce model complexity while maintaining its representational capacity, this article proposes a scale-aware pruning framework (SAPF) to enhance remote sensing object detection ability. First, this article classifies the convolutional layers in object detection models into two categories: layers with a single-scale feature representation and layers with a multiscale feature representation. For convolutional layers with single-scale features, we utilize singular value decomposition (SVD) to quantify feature importance and assess filter redundancy to enhance model efficiency. By removing less critical filters, this pruning criteria aims to reduce the model size and computational load without compromising performance. However, convolutional layers with multiscale features are crucial for optimizing feature extraction and balancing information capture across various scales. To address this, this article evaluates the similarity between convolutional layers with different scales to determine the contribution of various scale features in multiscale fusion. Surprisingly, the SAPF can reduce the FLOPs and parameters, as well as ensure the representational ability obviously when the YOLO v5s and Faster-RCNN are adopted to classify the NWPU VHR-10, RSOD, and SIMD datasets. This means we can save the training computation resources for the model. Additionally, SAPF can significantly improve the efficiency of the model in object detection to ensure its real-time performance.

With the exponential growth of satellite remote sensing data, land cover classification models must adapt continuously to new classes. However, conventional incremental learning methods face critical challenges: catastrophic forgetting degrades recognition of old classes, and the softmax function further suppresses old-class probabilities due to ”class crowding.” Existing distillation techniques also struggle to transfer features in irregular geospatial regions. To address these issues, we propose Soft Labels and Subregion Distillation (SLSRD). SLSRD mitigates class crowding by employing soft labels instead of hard labels, derived from a hybrid of softmax and sigmoid outputs that preserve richer probabilistic information. Concretely, the soft label is a convex combination of softmax- and sigmoid-based probabilities that preserves inter-class relations while relaxing over-confident exclusivity for newly introduced categories, and it supervises all pixels across stages. In parallel, a breadth-first search identifies subregions within each image, which are weighted by probability and size, and similarity between corresponding subregions of the old and new models is maximized. This dual strategy effectively transfers fine-grained knowledge and overcomes the limitations of conventional distillation methods, particularly for large-scale remote sensing imagery. Experiments on three benchmark datasets-Vaihingen, GID, and FBP-demonstrate that SLSRD outperforms traditional methods, significantly improving incremental land cover classification.

Land cover categories and features change with time. It generates demand for developing incremental learning methods for land cover classification. Meanwhile, due to the expensive cost of sample annotation, it is very difficult to obtain a large amount of annotated data for training. Therefore, how to effectively develop a few-shot incremental semantic segmentation method for land cover classification has become a significant task for remote sensing data interpretation. In this paper, we propose a novel data replay method with masked exemplar set (RMES) to improve land cover classification performance under the condition of few samples. It maintains a masked sample queue for each class. In this method, at the end of each learning step, two operations need to be run, the threshold sample filtering operation and the sample masking storage operation. These two operations update the sample queue and make it part of the training set in the next incremental learning stage. This alleviates overfitting and catastrophic forgetting problems. As a result of the experiment, the proposed RMES had superior performance in the CCF dataset.

Land cover classification (LCC) is an important application in remote sensing data interpretation. As two common data sources, SAR images can be regarded as an effective complement to optical images, which will reduce the influence caused by single-modal data. But common LCC methods are focusing on designing advanced network architectures to process single-modal remote sensing data. Few works have been oriented toward improving segmentation performance through fusing multi-modal data. In order to deeply integrate SAR and optical features, we propose SwinTFNet, a dual-stream deep fusion network. Through the global context modeling capability of Transformer structure, SwinTFNet models teleconnections between pixels in other regions and pixels in cloud regions for better prediction in cloud regions. In addition, a Cross-Attention Fusion Module (CAFM) is proposed to fuse features from optical and SAR data. Experimental results show that our method improves greatly in the classification of clouded images compared with other excellent segmentation methods and achieves the best performance on multi-modal data.

Convolutional neural networks will face the problem of catastrophic forgetting in the process of incremental learning. To solve this problem, we propose an incremental learning method called the strategy of edge removal and feature point aggregation, or ERFPA for short. In the cross-entropy loss, we perform edge detection and removal on the labels generated by the old model predictions, and then fuse them with the new labels. We calculate the mean point of different classes, and make the model learn features better by narrowing the distance with similar pixels. As demonstrated by the results of our experiment, on two remote sensing image datasets: CCF and Vaihingen, our method achieves state-of-the-art results.

The passive localization system (PLS) is fundamental to many wireless applications. The deployment of the monitoring stations plays a key role in the performance of the PLSes. However, the workflow of the emerging cutting-edge PLSes is becoming more flexible in the complicated environment, which makes it hard to optimize the deployment. To fulfill the requirement of the real-world applications, we propose a multiobjective PLS deployment optimization model, including a surrogate geometric dilution of precision (S-GDOP) model and a system coverage indicator to meet the demand for the detection performance of the known and unknown targets. The proposed S-GDOP is separable and open to various performance-related factors in this article. Motivated by the various cooperation mechanisms and the empirical deployment patterns, we propose a bilevel gene-based multiobjective memetic algorithm within the decomposition framework to solve this problem. By maintaining an adaptive multicomponent gene population (MCGP) and a local pivot (LP)-based local search, the population evolves on two precise and consecutive gene levels, which effectively utilizes the problem and evolution-related heuristic information. The proposed algorithm outperforms another four popular algorithms in 83.3% bilateral comparisons and obtains more implicit deployment patterns, clearer deployment structures, and better converged Pareto fronts.

During incremental learning tasks, catastrophic forgetting occurs when old models are updated with new information. To address this issue, we propose a novel method called label strategy and adaptive weights (LSAW) that improves the incremental learning process. The label strategy introduces the old classes and solves the problem of how to reasonably use the wrong samples predicted by the old model. In the cross-entropy (CE) loss, we apply a threshold to filter the pseudolabels predicted by the old model. Subsequently, we merge the pixel samples with high probability with the current label. The probability here refers to the probability that the pixel belongs to the true class. This process enables the introduction of information from old classes that are not directly accessible in the current stage. Moreover, this information is relatively reliable, and the model exhibits confidence in its accuracy. For the remaining pixels, we retain all classes’ information through label smoothing. In the distillation function, the old class and background pixel samples are selected for distillation according to the prediction map of the old classes. The weights of the classes are adaptively updated and adjusted using specific label information from each batch and the different stages of incremental learning. As demonstrated by the results of our experiment, on three remote sensing image datasets: China Computer Federation (CCF), Potsdam, and Vaihingen, our method achieves the best results.

The deployment of the airships plays a key role in maximizing the performance of the near space communication system. The main problem is how to strike a balance between the conflicting network speed and coverage for complex user distribution. In this paper, we propose a multiobjective deployment optimization model considering path loss, user demand, and inner structure. Under the framework of the multiobjective evolutionary algorithm (MOEA) based on decomposition (MOEA/D), we propose a similarity-based MOEA to optimize this problem. The proposed algorithm is motivated by the population's perception on the decision variable space. The proposed algorithm perceives the decision variable space by deploying airships to latent regions. The perceptions of different solutions are related by the similarity between their deployments and utilized differently by crossover and mutation. The proposed algorithm is tested on five designed problems compared with MOEA/D with the other popular reproduction operators. We also test the proposed scheme integrated with another two popular algorithms. The experimental results show that the similarity-based MOEA/D outperforms the other algorithms significantly in detecting hotspots, tracking multiple hotspots and safely deploying airships for most cases. The proposed scheme also works well with the other algorithms.

The Near Space Communication System is a promising and burgeoning alternative solution to the modern world's increasing communication demand. The deployment of the airships is important to the performance of the Near Space Communication System, of which the coverage and speed are conflictive. In a real world application, this system is also usually faced with regular changes of the user distribution. In this paper, we present a Tide User Model which simulates those changes of the user distribution. To optimize the deployment of the airships on the Tide User Model, a memetic algorithm based on MOEA/D with particularly designed operators is proposed aiming at providing a set of solutions as good as possible for the decision maker. We carry out the experiments on which the proposed algorithm and MOEA/D with two regular operators are compared for different settings. The results show that the proposed memetic algorithm based on MOEA/D achieves satisfying results and has better performance on the Tide User Model.

Multifactorial evolutionary algorithm is used to deal with multifactorial optimization problem which simultaneously optimizes multiple tasks. In this paper, we introduce particle swarm optimization operation into the multifactorial evolutionary algorithm, and propose a hybrid algorithm for multifactorial optimization. The major aim is to utilize particle swarm optimization operation to accelerate the convergence and improve the accuracy of solutions. Experimental comparisons between the proposed hybrid algorithm and the original multi-factorial evolutionary algorithm show that the particle swarm update operators can effectively accelerate the convergence on some benchmark problems.

Social networks are effective tools for analyzing many social topics in sociology. In the past few decades, a great deal of efforts have been made to study the balance property of social networks. This paper presents a novel bi-objective model for social network structural balance, and a multiobjective discrete particle swarm optimizer is used to optimize the bi-objective model. Each single run of the algorithm can yield a set of Pareto solutions, each of which represents a certain network partition that divides a signed network into many clusters. Consequently, by simultaneously optimizing the objectives in the proposed model, one may have many choices to analyze the balance problem. Extensive experiments compared against several other models and algorithms have been done. All the experiments indicate that the proposed model is helpful for social network structural balance analytics, and that the algorithm is effective.

In this paper, we propose a novel approach for detecting multiple changes from two multi-temporal images. Despite the development of the change vector analysis (CVA) framework and its improved version the compressed CVA (C2VA) framework, it is found that they are limited when tackling the multi-change detection task for the images with one channel. Also, the intensity itself is fragile due to the existing noise, which especially influences the detection of subtle changes. Therefore, the stacked denosing autoencoder (SDAE) which serves as a fine tool for feature extraction is employed to generate a multi-dimensional feature representations. In this way, the C2VA framework can be applied to the inner robust features so that a satisfactory performance can be guaranteed. Experimental results from two datasets show its high accuracy and moderate time complexity, which demonstrates the effectiveness of the proposed SDAE-C2VA approach.

Deep community can be detected by removing noise nodes or edges from a network. A centrality measure, named local Fiedler vector centrality is proposed for deep community detection. Algorithms to optimize local Fiedler vector centrality are either with high computation complexity or difficult to find the optimal solution of local Fiedler vector centrality. In this paper, a novel memetic algorithm is proposed to maximize local Fiedler vector centrality for deep community detection. Experiments of our proposed memetic algorithm on four real world networks demonstrate that our algorithm can find optimal solution of local Fiedler vector centrality and is effective to discover deep communities.

The flexible job-shop scheduling problem (FJSP) is an extension of the classical job scheduling which is concerned with the determination of a sequence of jobs, consisting of many operations, on different machines, satisfying parallel goals. This paper addresses the FJSP with two objectives: Minimize makespan, Minimize total operation cost. We introduce a memetic algorithm based on the Nondominated Neighbor Immune Algorithm (NNIA), to tackle this problem. The proposed algorithm adds, to NNIA, local search procedures including a rational combination of undirected simulated annealing (UDSA) operator, directed cost simulated annealing (DCSA) operator and directed makespan simulated annealing (DMSA) operator. We have validated its efficiency by evaluating the algorithm on multiple instances of the FJSPs. Experimental results show that the proposed algorithm is an efficient and effective algorithm for the FJSPs, and the combination of UDSA operator, DCSA operator and DMSA operator with NNIA is rational.

The near space communication system is a burgeoning communication system. This system has many advantages over the satellite and terrestrial networks. Being built on the near space airships, the deployment of the airships has a significant impact on the performance of the system. Various factors should be taken into consideration to build such a system of which some objectives relate with each other and specific areas weight objectives differently. The evolutionary multiobjective optimization can fulfill the purpose to provide a series of choices of the deployment scheme. In this paper, a model of such a system is proposed and the deployment of airships is solved using the multiobjective evolutionary algorithm based on decomposition. Cases with different numbers of airships are tested and the Pareto fronts are obtained. In order to increase the density of the Pareto front, a local search method based on the positions of the airships is proposed. The experiment shows that the local search method can effectively increase the number of Pareto solutions obtained.